Advanced Program in Generative AI and Machine Learning
Gain essential skills to build and scale generative AI and machine learning solutions. This course is exclusively for participants based in India.
What you will learn
Gain contemporary skills and knowledge for your job now.
This course is exclusively for participants based in India.
Generative AI is moving quickly from experimentation to enterprise adoption, creating strong demand for advanced AI/ML expertise. This program will equip you with the skills needed to design, deploy, and scale AI/ML solutions responsibly, to lead innovation, and deliver measurable ROI.
Designed for professionals in India who want to transition into AI/ML roles or advance their current projects, this part time program runs for 9 months, with a commitment of 8–10 hours per week. It follows a blended format of recorded online lectures and weekly live sessions and includes a 2-day in-person immersion at the Melbourne Global Centre in Delhi.
Co-designed with Emeritus, a leading provider of affordable high-quality education, you'll gain hands-on expertise across the latest tools and libraries in AI and machine learning, including TensorFlow, PyTorch, Hugging Face, and Kubernetes.
Gain essential skills in data science and Python programming
Build a solid understanding of core concepts in data science, including Python programming and key statistical methods. You'll apply your acquired knowledge of mathematics, statistics, and programming to develop AI and Machine learning (ML) applications, essential foundations in the implementation and usage of GenAI (generative AI).
Learn valuable machine learning techniques
Explore key machine learning approaches, including supervised and unsupervised techniques. Learn how to apply these techniques to real-world datasets and improve accuracy, enhance performance, and support better decision-making in AI and generative AI projects.
Discover how deep learning transforms key AI applications
Get hands-on experience with deep learning models, including CNNs (convolutational neural networks), RNNs (recurrent neural networks) and encoder-decoder transformers, and learn how these architectures are revolutionsing image recognition and natural language understanding, which are fundamental in GenAI systems.
Create responsible, real-world AI solutions
Master generative AI, large language models, and retrieval-augmented generation (RAG), and discover how to apply these technologies responsibly, following ethical AI principles to create trustworthy, real-world solutions that drive innovation.
Who you will learn from
Learn from skilled academics and professional experts who will share invaluable knowledge you can use in your job.
Dr Mel Mistica
Senior Research Fellow and Data Specialist in Natural Language Processing
Mel Mistica is a cross-disciplinary researcher with expertise in Computational Linguistics and Natural Language Processing (NLP), combining academic knowledge with industry experience. Mel works as a Research Data Specialist with MDAP, is an active member of the Natural Language Processing group and contributes to the University’s Centre for Artificial Intelligence and Digital Ethics.
Professor Jeannie Paterson
Professor of Law (consumer protection and AI regulation)
Jeannie Paterson is Professor of Law and Director of the Centre for AI and Digital Ethics at the University of Melbourne. Her research focuses on consumer law, regulatory design, and AI’s role in justice, misinformation, and human–AI interaction. A Fellow of the Australian Academy of Law, she serves on committees for the Victorian Legal Services Board, Beyond Blue, CPRC, OMIX3, and Orygen. In 2024 she was a member of the Australian Government’s AI Advisory Committee.
Professor Eduard Hovy
Executive Director of Melbourne Connect, Professor in Computing and Information Systems
Professor Eduard Hovy is Executive Director of Melbourne Connect and Professor in Computing and Information Systems at the University of Melbourne, and Adjunct Professor at Carnegie Mellon. Formerly a DARPA Program Manager, he earned his PhD at Yale and holds honorary doctorates from UNED Madrid and the University of Antwerp. An ACL and AAAI Fellow, he has authored 8 books and 400+ papers, with 68,000 citations.
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Course details
This program is designed for software engineers seeking to transition into AI/ML roles or enhance existing projects.
It will also be valuable to professionals skilled in the following roles:
- Data scientistsand data analysts aiming to expand their expertise with advanced AI/ML and generative AI.
- Business analystsand consultantslooking to leverage AI for insights and decision-making.
- Product Managers and product owners wanting to incorporate AI/ML into product development and strategy.
Though not essential, a relevant bachelor’s degree (or higher) along with foundational knowledge in mathematics and programming is recommended.
Advanced Program in Generative AI and Machine Learning addresses the urgent industry need for talent capable of building, deploying, and scaling AI/ML solutions.
it equips learners with a range of advanced skills in generative AI and machine learning, including:
- Hands-on expertise across latest tools and libraries (e.g., TensorFlow, PyTorch, Hugging Face, Kubernetes)
- Real-world practical experience relevant to sectors such as finance, retail, healthcare, and HR.
- Optional IBM certifications in TensorFlow, responsible GenAI, and developing GenAI applications using Python
- Structured career support including GitHub portfolio building, IIMJobs Pro membership, and interview preparation
This course ensures that learners are job-ready and positioned to lead AI-driven transformation across functions.
- Introduction to data science and Python fundamentals
- Data wrangling, analysis, and feature engineering
- Regression, classification, clustering, recommendation systems
- Neural networks, CNNs, RNNs, transformers
- Natural language processing and computer vision applications
- Generative AI: GANs, VAEs, and advanced LLMs
- Agentic AI and RAG systems for contextual decision-making
- Reinforcement learning and deep RL
- Deployment, MLOps, Streamlit applications
- Ethical AI and regulatory frameworks
Upon completion of this course, you'll be able to:
- Explain the basics of Data Science, including its applications, learning path, and importance.
- Apply your understanding of mathematics, statistics, and programming to implement core AI and ML algorithms
- Analyse techniques and methods in Python to read, preprocess, and turn data into relevant features
- Select the most suitable machine learning technique (e.g., regression, classification) to solve a specific real-world problem
- Use deep learning models like CNNs and RNNs for tasks like image processing or sequence analysis
- Evaluate advanced neural network models such as Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequence analysis.*
- Develop NLP models, including the use of transformer architectures for tasks like text generation and translation
- Implement computer vision and speech recognition systems using advanced deep learning techniques.
- Apply principles of reinforcement learning to create intelligent agents capable of decision-making in complex environments.
- Deploy ML models using tools like Flask & Streamlit to create interactive web applications showcasing AI and ML solutions.
This course runs for 9 months and requires a time commitment of approximately 8 to 10 hours per week. Predominately delivered online, the course features recorded lectures, live sessions, projects, assignments, faculty masterclasses, labs, and in-person campus immersion at the Melbourne Global Centre in Delhi.
Assessment: Combination of graded assignments and a final capstone project. Learners must score minimum 70% across assessments and successfully complete the capstone project to be awarded their course completion certificate.
Advanced Program in Generative AI and Machine Learning delivers a comprehensive, industry-aligned curriculum spanning the fundamentals of data science, core machine learning, advanced deep learning, computer vision, natural language processing, and emerging domains such as generative AI, large language models (LLMs), reinforcement learning, MLOps, and agentic AI.
Designed exclusively for participants residing in India, the course includes a 2-day immersion program at the prestigious Melbourne Global Centre — Delhi.